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In silico screening of Fyn kinase inhibitors using classification-based QSAR model, molecular docking, molecular dynamics and ADME study

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Abstract

This study aimed to use a computational approach that combined the classification-based QSAR model, molecular docking, ADME studies, and molecular dynamics (MD) to identify potential inhibitors of Fyn kinase. First, a robust classification model was developed from a dataset of 1,078 compounds with known Fyn kinase inhibitory activity, using the XGBoost algorithm. After that, molecular docking was performed between potential compounds identified from the QSAR model and Fyn kinase to assess their binding strengths and key interactions, followed by MD simulations. ADME studies were additionally conducted to preliminarily evaluate the pharmacokinetics and drug-like characteristics of these compounds. The results showed that our obtained model exhibited good predictive performance with an accuracy of 0.95 on the test set, affirming its reliability in identifying potent Fyn kinase inhibitors. Through the application of this model in conjunction with molecular docking and ADME studies, nine compounds were identified as potential Fyn kinase inhibitors, including 208 (ZINC70708110), 728 (ZINC8792432), 734 (ZINC8792187), 736 (ZINC8792350), 738 (ZINC8792286), 739 (ZINC8792309), 817 (ZINC33901069), 852 (ZINC20759145), and 1227 (ZINC100006936). MD simulations further demonstrated that the four most promising compounds, 728, 734, 736, and 852 exhibited stable binding with Fyn kinase during the simulation process. Additionally, a web-based platform (https://fynkinase.streamlit.app/) has been developed to streamline the screening process. This platform enables users to predict the activity of their substances of interest on Fyn kinase from their SMILES, using our classification-based QSAR model and molecular docking.

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Data availability

The data in this study is available on our GitHub repository at https://github.com/phuongnvp/fyn_kinase.

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Funding

This research did not receive dedicated funding from any specific grant provided by public, commercial, or not-for-profit sectors.

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Authors and Affiliations

  1. Department of Pharmacognosy, Faculty of Pharmacognosy and Traditional Medicine, Hanoi University of Pharmacy, Hanoi, 11000, Vietnam

    Nguyen Thu Hang, Thai Doan Hoang Anh & Nguyen Van Phuong

  2. Department of Analytical Chemistry and Standardization, National Institute of Medicinal Materials, 3B Quang Trung, Hanoi, 10000, Vietnam

    Le Nguyen Thanh

  3. Institute of Information Technology, Vietnam Academy of Science and Technology, 18 Hoang Quoc Viet, Cau Giay, Hanoi, 10000, Vietnam

    Nguyen Viet Anh

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  1. Nguyen Thu Hang
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Contributions

Nguyen Thu Hang: Conceptualization, Methodology, Writing—Review & Editing; Thai Doan Hoang Anh: Formal analysis, Investigation, Writing—Original Draft, Writing—Review & Editing; Le Nguyen Thanh: Software, Writing—Review & Editing; Nguyen Viet Anh: Software, Writing—Review & Editing; Nguyen Van Phuong: Conceptualization, Methodology, Writing—Original Draft, Writing—Review & Editing.

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Correspondence to Nguyen Van Phuong.

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Hang, N.T., Anh, T.D.H., Thanh, L.N. et al. In silico screening of Fyn kinase inhibitors using classification-based QSAR model, molecular docking, molecular dynamics and ADME study. Mol Divers (2024). https://doi.org/10.1007/s11030-024-10905-w

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